GNSS/INS/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization

Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct.

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“…More details about the reference systems can be seen in [35,36]. The b-frame and l-frame are precalibrated to achieve spatial synchronization.…”

Section: Methodsmentioning

confidence: 99%

“…All the coordinate transformations in this paper are . The rigid body transformation operation is defined as [35]:…”

Section: Methodsmentioning

confidence: 99%

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

Qian

Zhang

et al. 2020

Remote Sensing

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“…Compared with the former, the latter is more accurate and robust, but time-consuming [ 18 ]. In our previous work [ 19 ], we optimized IMU (inertial measurement unit) information fusion based on a cartographer [ 16 ]. However, the matching effect by the 3D probabilistic map matching [ 19 ] is not good in elevation, roll, and pitch for the LiDAR, such as VLP-16 whose vertical resolution is low.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [ 19 ], we optimized IMU (inertial measurement unit) information fusion based on a cartographer [ 16 ]. However, the matching effect by the 3D probabilistic map matching [ 19 ] is not good in elevation, roll, and pitch for the LiDAR, such as VLP-16 whose vertical resolution is low. Moreover, in areas where environmental features are limited and GNSS are not available, such as is the tunnel case in Figure 1 , the GNSS/IMU/LiDAR-SLAM integrated navigation system cannot work well.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, such kind of vehicle aiding information can be added into the GNSS/IMU/LiDAR-SLAM system for a more comprehensive navigation performance. Therefore, in view of the low vertical resolution of VLP-16 and the possible failure of LiDAR-SLAM, this paper implemented a GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system based on the LeGO-LOAM (Lightweight and ground-optimized lidar odometry and mapping) [ 5 ] feature matching method and the solution [ 19 ] for road environments. Odometer information is usually converted to the forward speed for assistance [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

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GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

Chang

Niu

Liu

2020

Sensors

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In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.

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Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data

Shmaliy

Shen

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In this paper, a tightly-coupled light detection and ranging (LiDAR)/dead reckoning (DR) navigation system with uncertain sampling time is designed for mobile robot localization. The Kalman filter (KF) is used as the main data fusion filter, where the state vector is composed of the position error, velocity error, yaw, and sampling time. The observation is provided of the difference between the LiDAR-derived and DR-derived distances measured from the corner feature points (CFPs) to the mobile robot. A real test experiment has been conducted to verify a good performance of the proposed method and show that it allows for a higher accuracy compared to the traditional LiDAR/DR integration.

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GNSS/INS/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization

Cited by 112 publications

References 26 publications

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data

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